Supporting self-adaptation in streaming data mining applications

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Supporting self-adaptation in streaming data mining applications
There are many application classes where the users are flexible with respect to the output quality. At the same time, there are other constraints, such as the need for real-time or interactive response, which are more crucial. This paper presents and evaluates a runtime algorithm for supporting adaptive execution for such applications. The particular domain we target is distributed data mining on streaming data. This work has been done in the context of a middleware system called GATES (Grid-based AdapTive Execution on Streams) that we have been developing. The self-adaptation algorithm we present and evaluate in this paper has the following characteristics. First, it carefully evaluates the longterm load at each processing stage. It considers different possibilities for the load at a processing stage and its next stages, and decides if the value of an adaptation parameter needs to be modified, and if so, in which direction. To find the ideal new value of an adaptation parameter, i...
Liang Chen, Gagan Agrawal
Added 12 Jun 2010
Updated 12 Jun 2010
Type Conference
Year 2006
Where IPPS
Authors Liang Chen, Gagan Agrawal
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